Bobcat Migration¶

Species Description¶

Bobcats are part of the larger genus of Lynx. There are 4 extant species of Lynx, one of which is the bobcat. The bobcat is often confused with the Lynx, but the key difference is that Lynx are larger and their tails are all black versus the Bobcat which have white tails with a black strip.

I chose the bobcat 'lynx rufus' to look at their migration because I have a sister who works at the Sonoma County Wildlife Rescue in California. She does the rescue and release part of the porgram and they frequently rescue injured bobcats, or get in baby bobcats that were abandoned in some way that they care for until they are old enough to be released. I however, am in Colorado and I have seen bobcats in the wilderness. This made me curious about their overall migration.

This species's migration ranges between Mexico and Canada, with the majority of the bobcats being found in the U.S. They are not a threatened species, but do suffer from habitat loss. They "live in a wide variety of habitats, including boreal coniferous and mixed forests in the north, bottomland hardwood forests and coastal swamps in the southeast, and desert and scrublands in the southwest"(The Smithsonian National Zoo & Conservation Biology Institute). Another important note that will inform the later interpretation is that bobcats do not hibernate and are most active during the winter, particularly January and February which is their mating season (The Wildlife Rescue Legaue).

If you are insterested in learning more about bobcats please visit The Smithsonian's National Zoo & Conservation Biology Institute and The Wildlife Rescue League .

Data Description¶

There were two sources of data used for this project - Ecoregions and GBIF data. Ecoregions data is available via a shapefile which can be downloaded here . The GBIF data is for species occurances. Together, the two can convey the migration spatially and over time of a certain species (if that species' data is available from GBIF).

  • Ecoregion Data¶

    Ecoregions "are areas that are geographically and ecologically similar and experience similar levels of solar radiation and precipitation". There are 846 Ecoregions globally, but most species would be in a small portion of those edoregions depending on their habitats and migration patterns. The Ecoregion shapefile used is an updated version from 2017 that was developed by experts.

Ecoregion Citation:

Terauds, Aleks, et al. “Announcing the Release of Ecoregion Snapshots.” >One Earth, One Earth, 31 May 2024, >www.oneearth.org/announcing-the-release-of-ecoregion-snapshots/.

  • GBIF Occurences Data¶

GBIF stands for Global Biodiversity Information Facility, it "is an international network and data infrastructure funded by the world's governments and aimed at providing anyone, anywhere, open access to data about all types of life on Earth"(Data Info citation below). This network draws many data sources together including museum specimans, DNA barcodes, and crowdsourced photos from smartphones used by non-experts and experts. GBIF uses data standards to index all these species records.

The data can vary:

  1. There may be more occurances recorded in National Parks compared to the Arctic, even if the species may similarly present in different regions. This is because there are fewer people to observe in the arctic.

  2. There may be greater or fewer occurances depending on time of year that people go outside, how accesible a region is during different times of year, etc.

  3. There may be variation depending on how many people want to provide/upload data and that also depends on what that person knows or likes more - one may be more likely to upload species they like or know.

Because this data laregly comes from crowdsourcing and the data will need to be normalized which will be further explained in the Methods Description.

GBIF Citation:

  • Data Info:

    https://www.gbif.org/what-is-gbif

  • Data:

    GBIF.org (12 October 2024) GBIF Occurrence Download https://doi.org/10.15468/dl.sye4x3

Methods Description¶

Because the GBIF data can vary by space and time, the data needs to be normalized to account for these issues that would otherwise skew the plot (ex. make it look like there are more observations in a certain area or time of year, when that wouldn't be an accurate reflection of reality). The data was normalized by ecoregion samples, by month samples, and by area. This way the normalization is over space and time (This should help control for the number of active observers in each location and time of year.)

  1. Before normalization can happen, the GBIF data was converted into a GeoDataFrame using latitude and longitude geometry.

  2. Next, a spatial join was performed with parameters (how= 'inner', predicate= 'contains'). This identifies the Ecoregion for each observation.

  3. Next, observations were grouped by Ecoregion, selecting only month/ecosystem combinations that have more than one occurance recorded (since a single occurence could be an error). The .groupby() and .mean() methods were used to compute the mean occurences by ecoregion and by month.

  4. Lastly, divide occurrences by the mean occurrences by month AND the mean occurrences by ecoregion.

This normalizes the data to be able to have a more accurate plot.

Access locations and times of Bobcat encounters¶

I used a database called the Global Biodiversity Information Facility (GBIF). GBIF is compiled from species observation data all over the world, and includes everything from museum specimens to photos taken by citizen scientists in their backyards.

Set up code to prepare for download¶

I will be getting data from a source called GBIF (Global Biodiversity Information Facility). I need a package called pygbif to access the data, which may not be included in my environment. Install it by running the cell below:

In [1]:
%%bash
pip install pygbif

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Import Packages¶

Import packages that will help with

  1. reproducible file paths
  2. tabular data
In [2]:
import os
import pathlib
import time
import zipfile
from getpass import getpass
from glob import glob

import pandas as pd
import pygbif.occurrences as occ
import pygbif.species as species
In [3]:
# Create data directory in the home folder
data_dir_bobcat = os.path.join(
    # Home directory
    pathlib.Path.home(),
    # Earth analytics data directory
    'earth-analytics',
    'data',
    # Project directory
    'species_distribution_bobcat',
)
os.makedirs(data_dir_bobcat, exist_ok=True)

# Define the directory name for GBIF data
gbif_dir_bobcat = os.path.join(data_dir_bobcat, 'gbif_bobcat')

Check the location for the data_dir_bobcat¶

In [4]:
data_dir_bobcat
Out[4]:
'/home/jovyan/earth-analytics/data/species_distribution_bobcat'

Register and log in to GBIF¶

I need a GBIF account to complete this challenge. Then, run the following code to save my credentials on your computer.

Warning

My email address must match the email I used to sign up for GBIF!

Tip

If I accidentally enter your credentials wrong, I can set reset_credentials=True instead of reset_credentials=False.

In [5]:
reset_credentials = False
# GBIF needs a username, password, and email
credentials = dict(
    GBIF_USER=(input, 'GBIF username:'),
    GBIF_PWD=(getpass, 'GBIF password'),
    GBIF_EMAIL=(input, 'GBIF email')
)
for env_variable, (prompt_func, prompt_text) in credentials.items():
    # Delete credential from environment if requested
    if reset_credentials and (env_variable in os.environ):
        os.environ.pop(env_variable)
    # Ask for credential and save to environment
    if not env_variable in os.environ:
        os.environ[env_variable] = prompt_func(prompt_text)

Check and make sure you username is correct¶

Also double check that the password has been saved

In [6]:
os.environ ['GBIF_USER']
Out[6]:
'brglea'
In [7]:
!echo $GBIF_USER

brglea
In [8]:
'GBIF_PWD' in os.environ
Out[8]:
True

Get the species key¶

  1. Replace the species_name with the name of the species you want to look up
  2. Run the code to get the species key
In [9]:
# Query species
species_info = species.name_lookup('lynx rufus', rank='SPECIES')

# Get the first result
first_result = species_info['results'][0]

# Get the species key (nubKey)
species_key = first_result['nubKey']

# Check the result
first_result['species'], species_key
Out[9]:
('Lynx rufus', 2435246)

Download data from GBIF¶

Submit a request to GBIF

  1. Replace csv_file_pattern with a string that will match any .csv file when used in the glob function. HINT: the character * represents any number of any values except the file separator (e.g. /)

  2. Add parameters to the GBIF download function, occ.download() to limit your query to:

    • observations
    • from 2023
    • with spatial coordinates.

  3. Then, run the download. This can take a few minutes. :::

In [10]:
# Only download once
gbif_pattern = os.path.join(gbif_dir_bobcat, '*.csv')
if not glob(gbif_pattern):
    # Only submit one request
    if not 'GBIF_DOWNLOAD_KEY' in os.environ:
        # Submit query to GBIF
        gbif_query = occ.download([
            "speciesKey = 2435246",
            "hasCoordinate = True",
            "year = 2023",
        ])
        os.environ['GBIF_DOWNLOAD_KEY'] = gbif_query[0]

    # Wait for the download to build
    download_key = os.environ['GBIF_DOWNLOAD_KEY']
    wait = occ.download_meta(download_key)['status']
    while not wait=='SUCCEEDED':
        wait = occ.download_meta(download_key)['status']
        time.sleep(5)

    # Download GBIF data
    download_info = occ.download_get(
        os.environ['GBIF_DOWNLOAD_KEY'], 
        path=data_dir_bobcat)

    # Unzip GBIF data
    with zipfile.ZipFile(download_info['path']) as download_zip:
        download_zip.extractall(path=gbif_dir_bobcat)

# Find the extracted .csv file path (take the first result)
gbif_path = glob(gbif_pattern)[0]

Check the gbif_path¶

In [11]:
gbif_path
Out[11]:
'/home/jovyan/earth-analytics/data/species_distribution_bobcat/gbif_bobcat/0010842-241007104925546.csv'

Load the GBIF data into Python¶

Load GBIF data

  1. Look at the beginning of the file I downloaded using the code below. The delimiter is a tab.
  2. Run the following code cell.
  3. Uncomment and modify the parameters of pd.read_csv() below until my data loads successfully and I have only the columns I want.

I can use the following code to look at the beginning of my file:

In [12]:
!head -n 2 $gbif_path 

gbifID	datasetKey	occurrenceID	kingdom	phylum	class	order	family	genus	species	infraspecificEpithet	taxonRank	scientificName	verbatimScientificName	verbatimScientificNameAuthorship	countryCode	locality	stateProvince	occurrenceStatus	individualCount	publishingOrgKey	decimalLatitude	decimalLongitude	coordinateUncertaintyInMeters	coordinatePrecision	elevation	elevationAccuracy	depth	depthAccuracy	eventDate	day	month	year	taxonKey	speciesKey	basisOfRecord	institutionCode	collectionCode	catalogNumber	recordNumber	identifiedBy	dateIdentified	license	rightsHolder	recordedBy	typeStatus	establishmentMeans	lastInterpreted	mediaType	issue
4953158569	50c9509d-22c7-4a22-a47d-8c48425ef4a7	https://www.inaturalist.org/observations/151699352	Animalia	Chordata	Mammalia	Carnivora	Felidae	Lynx	Lynx rufus		SPECIES	Lynx rufus (Schreber, 1777)	Lynx rufus		US		California	PRESENT		28eb1a3f-1c15-4a95-931a-4af90ecb574d	34.205588	-118.36292	28846.0						2023-03-16T03:21	16	3	2023	2435246	2435246	HUMAN_OBSERVATION	iNaturalist	Observations	151699352		Devon	2023-03-20T04:33:39	CC_BY_NC_4_0	Devon	Devon			2024-10-12T11:10:13.765Z	StillImage	COORDINATE_ROUNDED;CONTINENT_DERIVED_FROM_COORDINATES;TAXON_MATCH_TAXON_ID_IGNORED
In [13]:
# Load the GBIF data
bobcat_gbif_df = pd.read_csv(
    gbif_path, 
    delimiter='\t',
    index_col='gbifID',
    usecols=['gbifID', 'month', 'decimalLatitude', 'decimalLongitude']
)
bobcat_gbif_df.head()
Out[13]:
decimalLatitude decimalLongitude month
gbifID
4953158569 34.205588 -118.362920 3
4953055247 36.537634 -121.890603 10
4953008628 44.122360 -119.848505 11
4952902566 34.270494 -118.320036 9
4952869276 41.546645 -72.608720 1

Import geopandas to use in next cell¶

In [14]:
import geopandas as gpd

Download and save ecoregion boundaries¶

The ecoregion boundaries take some time to download – they come in at about 150MB. To use my time most efficiently, I need to complete caching the ecoregions data on the machine I'm working on so that I only have to download once. To do that, I'll also use conditionals, or code that adjusts what it does based on the situation.

Get ecoregions boundaries href="https://www.geographyrealm.com/terrestrial-ecoregions-gis-data/">get

  1. Find the URL for for the ecoregion boundary Shapefile. I can <a

ecoregion boundaries from Google.. 2. Change all the variable names to descriptive variable names, making sure to correctly reference variables I created before. 3. Run the cell to download and save the data.

In [15]:
# Set up the ecoregion boundary URL
ecoregions_url = (
 "https://storage.googleapis.com/teow2016"
 "/Ecoregions2017.zip")

# Set up a path to save the data on your machine
ecoregions_dir = os.path.join(data_dir_bobcat, 'resolve_ecoregions')

# Make the ecoregions directory
os.makedirs(ecoregions_dir, exist_ok=True)

# Join ecoregions shapefile path
ecoregions_path = os.path.join(ecoregions_dir, 'ecoregions.shp')

# Only download once
if not os.path.exists(ecoregions_path):
    ecoregions_gdf = gpd.read_file(ecoregions_url)
    ecoregions_gdf.to_file(ecoregions_path)

Make sure that worked! Use a bash command called find to look for all the files in my project directory with the .shp extension:

In [16]:
%%bash
find ~/earth-analytics/data/species_distribution_bobcat -name '*.shp'

/home/jovyan/earth-analytics/data/species_distribution_bobcat/resolve_ecoregions/ecoregions.shp

Load Ecoregions into Python¶

Download and save ecoregion boundaries from the EPA:

  1. Make a quick plot with .plot() to make sure the download worked.
  2. Run the cell to load the data into Python
In [17]:
# Open up the ecoregions boundaries
ecoregions_gdf = gpd.read_file(ecoregions_path)

# Name the index so it will match the other data later on
ecoregions_gdf.index.name = 'ecoregion'

# Plot the ecoregions to check download
ecoregions_gdf.plot(edgecolor='black', color='lightgreen')
Out[17]:
<Axes: >
No description has been provided for this image
In [18]:
ecoregions_gdf.head()
Out[18]:
OBJECTID ECO_NAME BIOME_NUM BIOME_NAME REALM ECO_BIOME_ NNH ECO_ID SHAPE_LENG SHAPE_AREA NNH_NAME COLOR COLOR_BIO COLOR_NNH LICENSE geometry
ecoregion
0 1.0 Adelie Land tundra 11.0 Tundra Antarctica AN11 1 117 9.749780 0.038948 Half Protected #63CFAB #9ED7C2 #257339 CC-BY 4.0 MULTIPOLYGON (((158.7141 -69.60657, 158.71264 ...
1 2.0 Admiralty Islands lowland rain forests 1.0 Tropical & Subtropical Moist Broadleaf Forests Australasia AU01 2 135 4.800349 0.170599 Nature Could Reach Half Protected #70A800 #38A700 #7BC141 CC-BY 4.0 MULTIPOLYGON (((147.28819 -2.57589, 147.2715 -...
2 3.0 Aegean and Western Turkey sclerophyllous and m... 12.0 Mediterranean Forests, Woodlands & Scrub Palearctic PA12 4 785 162.523044 13.844952 Nature Imperiled #FF7F7C #FE0000 #EE1E23 CC-BY 4.0 MULTIPOLYGON (((26.88659 35.32161, 26.88297 35...
3 4.0 Afghan Mountains semi-desert 13.0 Deserts & Xeric Shrublands Palearctic PA13 4 807 15.084037 1.355536 Nature Imperiled #FA774D #CC6767 #EE1E23 CC-BY 4.0 MULTIPOLYGON (((65.48655 34.71401, 65.52872 34...
4 5.0 Ahklun and Kilbuck Upland Tundra 11.0 Tundra Nearctic NE11 1 404 22.590087 8.196573 Half Protected #4C82B6 #9ED7C2 #257339 CC-BY 4.0 MULTIPOLYGON (((-160.26404 58.64097, -160.2673...

Convert the GBIF data to a GeoDataFrame¶

To plot the GBIF data, I need to convert it to a GeoDataFrame first. This will make some special geospatial operations from geopandas available, such as spatial joins and plotting.

  1. Convert DataFrame to GeoDataFrame
  2. Run the code to get a GeoDataFrame of the GBIF data.
In [19]:
#Convert dataframe (df) into geo dataframe (gdf)
bobcat_gbif_gdf = (
    gpd.GeoDataFrame(
        bobcat_gbif_df, 
        geometry=gpd.points_from_xy(
            bobcat_gbif_df.decimalLongitude, 
            bobcat_gbif_df.decimalLatitude), 
        crs="EPSG:4326")
    # Select the desired columns
    [['month', 'geometry']]
)
bobcat_gbif_gdf
Out[19]:
month geometry
gbifID
4953158569 3 POINT (-118.36292 34.20559)
4953055247 10 POINT (-121.8906 36.53763)
4953008628 11 POINT (-119.8485 44.12236)
4952902566 9 POINT (-118.32004 34.27049)
4952869276 1 POINT (-72.60872 41.54664)
... ... ...
4011868162 1 POINT (-121.81632 37.43427)
4011836346 1 POINT (-122.44225 42.11348)
4011733344 1 POINT (-97.10298 32.58653)
4011611239 1 POINT (-121.7627 36.66162)
4011547228 1 POINT (-96.57154 32.53426)

3657 rows × 2 columns

Store the new version of your dataframe for other notebooks as needed¶

In [20]:
%store ecoregions_gdf bobcat_gbif_gdf

Stored 'ecoregions_gdf' (GeoDataFrame)
Stored 'bobcat_gbif_gdf' (GeoDataFrame)

Normalize Data¶

Identify the Ecoregion for Each Observation¶

Combine the ecoregions and the observations spatially using a method called .sjoin(), which stands for spatial join.

  1. Perform a spatial join
  2. Identify the correct values for the how= and predicate= parameters of the spatial join.
  3. Select only the columns I will need for my plot.
  4. Run the code.
In [21]:
gbif_ecoregion_gdf = (
    ecoregions_gdf
    # Match the CRS of the GBIF data and the ecoregions
    .to_crs(bobcat_gbif_gdf.crs)
    # Find ecoregion for each observation
    .sjoin(
        bobcat_gbif_gdf,
        how='inner', 
        predicate='contains')
    # Select the required columns
    [['month','ECO_NAME','gbifID', 'OBJECTID']]

    # rename columns as needed
    .reset_index()
    .rename(columns={
       'ECO_NAME': 'name',
       'gbifID': 'observation_id',
       'OBJECTID': 'object_id'})
)

gbif_ecoregion_gdf
Out[21]:
ecoregion month name observation_id object_id
0 16 10 Allegheny Highlands forests 4420901180 17.0
1 16 5 Allegheny Highlands forests 4116293969 17.0
2 16 7 Allegheny Highlands forests 4165965742 17.0
3 16 3 Allegheny Highlands forests 4535584198 17.0
4 16 2 Allegheny Highlands forests 4055045625 17.0
... ... ... ... ... ...
3592 833 6 Northern Rockies conifer forests 4438948603 839.0
3593 833 11 Northern Rockies conifer forests 4458403810 839.0
3594 833 2 Northern Rockies conifer forests 4067548544 839.0
3595 833 11 Northern Rockies conifer forests 4454006926 839.0
3596 833 9 Northern Rockies conifer forests 4414403437 839.0

3597 rows × 5 columns

Count the Observations in Each Ecroregion Each Month¶

Group observations by ecoregion

  1. Select only month/ecosystem combinations that have more than one occurrence recorded, since a single occurrence could be an error.
  2. Use the .groupby() and .mean() methods to compute the mean occurrences by ecoregion and by month.
  3. Run the code – it will normalize the number of occurrences by month and ecoregion
In [22]:
bobcat_occurrence_df = (
    gbif_ecoregion_gdf
    # For each ecoregion, for each month...
    .groupby(['ecoregion', 'month'])
    # ...count the number of occurrences
    .agg(occurrences=('observation_id', 'count'))
)

# Get rid of rare observations (possible misidentification?)
bobcat_occurrence_df = bobcat_occurrence_df[bobcat_occurrence_df.occurrences>1]

bobcat_occurrence_df

# Take the mean by ecoregion
mean_occurrences_by_ecoregion = (
     bobcat_occurrence_df
     .groupby(['ecoregion'])
     .mean()
 )
# Take the mean by month
mean_occurrences_by_month = (
     bobcat_occurrence_df
     .groupby(['month'])
     .mean()
 )

bobcat_occurrence_df
Out[22]:
occurrences
ecoregion month
16 5 3
9 2
10 2
32 1 3
2 8
... ... ...
832 3 2
9 2
833 2 2
10 2
11 2

407 rows × 1 columns

In [23]:
mean_occurrences_by_ecoregion
Out[23]:
occurrences
ecoregion
16 2.333333
32 6.000000
33 3.300000
34 2.750000
43 4.200000
... ...
783 8.416667
790 19.666667
793 3.000000
832 2.000000
833 2.000000

66 rows × 1 columns

In [24]:
mean_occurrences_by_month
Out[24]:
occurrences
month
1 10.129032
2 9.062500
3 9.303030
4 8.382353
5 8.540541
6 8.800000
7 7.517241
8 7.000000
9 7.800000
10 7.550000
11 6.952381
12 10.483871

Normalize the Observations¶

Divide occurrences by the mean occurrences by month AND the mean occurrences by ecoregion

In [25]:
# Normalize by space and time for sampling effort
bobcat_occurrence_df['norm_occurrences'] = (
    bobcat_occurrence_df 
    /mean_occurrences_by_month 
    /mean_occurrences_by_ecoregion
)
bobcat_occurrence_df
Out[25]:
occurrences norm_occurrences
ecoregion month
16 5 3 0.150542
9 2 0.109890
10 2 0.113529
32 1 3 0.049363
2 8 0.147126
... ... ... ...
832 3 2 0.107492
9 2 0.128205
833 2 2 0.110345
10 2 0.132450
11 2 0.143836

407 rows × 2 columns

Store the new version of your dataframe for other notebooks as needed¶

In [26]:
%store bobcat_occurrence_df

Stored 'bobcat_occurrence_df' (DataFrame)

Plot Data¶

Import Packages Needed¶

Import packages for making interactive maps with vector data as well as calendar in order to get month names that will be used when creating the]plot.

In [27]:
# Get month names
import calendar

# Libraries for Dynamic mapping
import cartopy
import cartopy.feature as cf
import cartopy.crs as ccrs
import geopandas as gpd
import geoviews as gv
import geoviews.feature as gf
import holoviews as hv
import hvplot.pandas
import panel as pn

/opt/conda/lib/python3.11/site-packages/dask/dataframe/__init__.py:42: FutureWarning: 
Dask dataframe query planning is disabled because dask-expr is not installed.

You can install it with `pip install dask[dataframe]` or `conda install dask`.
This will raise in a future version.

  warnings.warn(msg, FutureWarning)

Create a Simplified GeoDataFrame for Plotting¶

The code below will streamline plotting with hvplot by simplifying the geometry, projecting it to a Mercator projection that is compatible with geoviews, and cropping off areas in the Arctic.

Download and save ecoregion boundaries from the EPA:

  1. Simplify the ecoregions with .simplify(.05), and save it back to the geometry column.
  2. Change the Coordinate Reference System (CRS) to Mercator with .to_crs(ccrs.Mercator())
  3. Use the plotting code that is already in the cell to check that the plotting runs quickly (less than a minute) and looks the way you want.
In [28]:
# Simplify the geometry to speed up processing
ecoregions_gdf.geometry = ecoregions_gdf.simplify(
    .01, preserve_topology=False
    )

# Change the CRS to Mercator for mapping
ecoregions_gdf = ecoregions_gdf.to_crs(ccrs.Mercator())

# Check that the plot runs in a reasonable amount of time
ecoregions_gdf.hvplot(
    x='Longitude',
    y='Latitude',
    geo=True, 
    crs=ccrs.Mercator()
    )
Out[28]:

Map Migration Over Time¶

  1. If applicable, replace any variable names with the names I defined previously.
  2. Customize my plot with my choice of title, tile source, color map, and size.

My plot will probably still change months very slowly in my Jupyter notebook, because it calculates each month’s plot as needed. Open up the saved HTML file to see faster performance!

In [30]:
# Join the occurrences with the plotting GeoDataFrame
bobcat_occurrence_gdf = ecoregions_gdf.join(bobcat_occurrence_df)

# Get the plot bounds so they don't change with the slider
xmin, ymin, xmax, ymax = bobcat_occurrence_gdf.total_bounds

# Define the slider widget
slider = pn.widgets.DiscreteSlider(
    name='month', 
    options={calendar.month_name[i]: i for i in range(1, 13)}
)

# Plot occurrence by ecoregion and month
bobcat_migration_plot = (
    bobcat_occurrence_gdf
    .hvplot(
        c='norm_occurrences',
        groupby='month',
        # Use background tiles
        geo=True, crs=ccrs.Mercator(), tiles='CartoLight',
        title="Bobcat Migration Observations Over Time",
        x='Longitude',
        y='Latitude',
        xlim=(xmin, xmax), ylim=(ymin, ymax),
        frame_height=600,
        widgets={'month': slider},
        widget_location='bottom'
    )
)

# Save the plot
bobcat_migration_plot.save('bobcat_migration.html', embed=True)

# Show the plot
bobcat_migration_plot

                                               
WARNING:W-1005 (FIXED_SIZING_MODE): 'fixed' sizing mode requires width and height to be set: figure(id='p79613', ...)









Out[30]:




BokehModel(combine_events=True, render_bundle={'docs_json': {'9fdc9ea7-01a1-4a9b-945f-4a954eccd605': {'version…
























Bobcat Migration Over Time Plot- While this species does move around, they are not migratory. The map reflects the ecoregions they habitate as well as the time of year they would be more active.¶


The Bobcat Migration Overtime shows a predominance in the U.S. thoughout
most of the year and fewer occurences in Canada and Mexico. The Ecoregion
data is what allows us to see the visual geography and because a spatial
join was performed with the GBIF data, lets us see only the locations where
there is more than one occurence in an Ecoregion.


There is not a striaght forward migration pattern that may be seen in
other species that may 'fly south for the winter' leading to what I have
now read in multiple places is that - bobcats are not migratory. This
was not something I researched prior to delving into the data, but after
seeing the plot makes sense. The plot however, confirms some of the other
species information.


The sampling efforts for normalization that were taken to account for
GBIF data being largely crowdsourced and varying by time and space
thwarted a more skewed plot. The resulting plot above is reflective
of the nature of bobcats and when are where they are most active.


June through August tends to have fewer norm occurences in general compared to
the fall and winter months (for the Northern Hemisphere) which have higher
norm occurences. This speaks to the fact that bobcats don't hibernate and
winter is their mating season (The Wildlife
Rescue League
), so they would be more likely to be spotted
by a human while looking for a mate. Bobcats are also found around areas
often frequented for winter activites like snowshoeing.


The lack of a straight forward migration pattern speaks to the nature of
bobcats, like the much smaller domestic housecat, are territorial animals.
They tend to occupy the same milage over time, marking it, and only move
under extreme conditions or habitat loss (The Smithsonian's National Zoo
& Conservation Biology Institute).
Because they live in different types of forests, coastal swamps, and
scrubland, they would be threatend by wildfire and drought. There have
been an increase in wildfires in recent years in the Sierra Nevadas,
the Rocky Mountains, and possibly other forest areas which would threaten
or cause them to relocate, but not by choice.


The plot more so speaks to the nature of bobcats, how climate change
threatens their habitats, and how the data was collected (varies by ecroregion
and time of year), than it speaks to a distinct migration pattern.